feat:添加了基本策略。

alpha/价值投资策略.ipynb

@@ -0,0 +1,296 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 价值投资策略 — 构建与回测\n",
+    "\n",
+    "## 策略思路\n",
+    "价值投资（Value Investing）：买入相对于基本面被低估的股票。\n",
+    "本策略使用 **市盈率 PE 倒数（EP）** 与 **市净率 PB 倒数（BP）** 合成价值因子，\n",
+    "每月调仓，持有估值最低（价值因子最高）的一批股票。\n",
+    "\n",
+    "## 研究流程\n",
+    "1. 加载数据（价格 + PE/PB 估值指标）\n",
+    "2. 价值因子计算与 IC 分析\n",
+    "3. 分层回测验证因子有效性\n",
+    "4. 构建策略并回测\n",
+    "5. 绩效评估"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(0, '..')\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from alpha.config import AlphaConfig\n",
+    "from alpha.factors import FactorAnalyzer\n",
+    "from alpha.strategy import Strategy, QuantileSignal, EqualWeightAllocator\n",
+    "from alpha.backtest import BacktestEngine\n",
+    "from alpha.evaluation import PerformanceEvaluator\n",
+    "from alpha.data_loader import DataLoader\n",
+    "\n",
+    "%matplotlib inline\n",
+    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
+    "plt.rcParams['axes.unicode_minus'] = False\n",
+    "\n",
+    "print('模块导入成功')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. 加载数据\n",
+    "\n",
+    "价值因子需要估值数据（PE / PB），通过 `DataLoader.load_factor` 加载。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "START_DATE = '2020-01-01'\n",
+    "END_DATE   = '2024-12-31'\n",
+    "\n",
+    "loader = DataLoader()\n",
+    "\n",
+    "# 价格数据 + 估值数据\n",
+    "price_data = loader.load_prices(START_DATE, END_DATE)\n",
+    "pe_data  = loader.load_factor('pe_ttm', START_DATE, END_DATE)   # 市盈率 TTM\n",
+    "pb_data  = loader.load_factor('pb', START_DATE, END_DATE)       # 市净率\n",
+    "\n",
+    "print(f'价格数据: {price_data.shape}')\n",
+    "print(f'PE(TTM)数据: {pe_data.shape}')\n",
+    "print(f'PB数据: {pb_data.shape}')\n",
+    "\n",
+    "# 展示某天的估值截面\n",
+    "sample = pd.DataFrame({\n",
+    "    'PE_TTM': pe_data.iloc[-1],\n",
+    "    'PB': pb_data.iloc[-1],\n",
+    "}).dropna()\n",
+    "sample.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. 价值因子计算与 IC 分析\n",
+    "\n",
+    "价值因子 = 标准化后的 (1/PE + 1/PB)：\n",
+    "$$\n",
+    "Value = Z\\left(\\frac{1}{PE}\\right) + Z\\left(\\frac{1}{PB}\\right)\n",
+    "$$\n",
+    "PE/PB 越低 → 价值因子越高 → 越被低估。"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 剔除 PE/PB 非正值（亏损或净资产为负）\n",
+    "ep = 1.0 / pe_data.where(pe_data > 0)\n",
+    "bp = 1.0 / pb_data.where(pb_data > 0)\n",
+    "\n",
+    "# 截面标准化后合成\n",
+    "def zscore_cross_section(df):\n",
+    "    return (df - df.mean(axis=1)) / (df.std(axis=1) + 1e-12)\n",
+    "\n",
+    "value_factor = zscore_cross_section(ep) + zscore_cross_section(bp)\n",
+    "\n",
+    "# 未来 20 日收益率（价值因子预测周期较长）\n",
+    "fwd_20d = price_data.pct_change(20).shift(-20)\n",
+    "\n",
+    "factor_stacked = value_factor.stack()\n",
+    "fwd_stacked = fwd_20d.stack()\n",
+    "\n",
+    "analyzer = FactorAnalyzer(factor_stacked, fwd_stacked)\n",
+    "ic = analyzer.compute_ic(method='rank')\n",
+    "\n",
+    "print('价值因子 IC 汇总:')\n",
+    "for k, v in analyzer.ic_summary().items():\n",
+    "    print(f'  {k}: {v:.4f}')\n",
+    "\n",
+    "ic.plot(figsize=(12, 3), title='价值因子 Rank IC 时间序列')\n",
+    "plt.axhline(y=0, color='r', linestyle='--')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. 分层回测"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "quantile_ret = analyzer.quantile_returns(n_quantiles=5)\n",
+    "print('各分位组平均收益 (Q1=最贵, Q5=最便宜):')\n",
+    "print(quantile_ret)\n",
+    "\n",
+    "quantile_ret['avg_return'].plot(kind='bar', figsize=(8, 4), color='steelblue')\n",
+    "plt.title('价值因子分层收益 (未来20日)')\n",
+    "plt.ylabel('平均收益率')\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. 构建策略并回测"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 回测配置\n",
+    "config = AlphaConfig(\n",
+    "    initial_cash=1_000_000,\n",
+    "    commission_rate=0.0003,\n",
+    "    slippage=0.001,\n",
+    "    stamp_tax=0.001,\n",
+    ")\n",
+    "\n",
+    "# 价值策略：持有价值因子最高的 20% 股票（最被低估）\n",
+    "strategy = Strategy(\n",
+    "    name='价值投资策略-EP+BP',\n",
+    "    factors=[],\n",
+    "    signal_generator=QuantileSignal(\n",
+    "        n_quantiles=5, long_quantile=5, short_quantile=0,\n",
+    "    ),\n",
+    "    weight_allocator=EqualWeightAllocator(max_positions=10),\n",
+    "    description='每月持有EP+BP合成的价值因子最高的20%股票'\n",
+    ")\n",
+    "\n",
+    "engine = BacktestEngine(config)\n",
+    "equity_curve = engine.run(\n",
+    "    strategy=strategy,\n",
+    "    price_data=price_data,\n",
+    "    factor_data={'value': value_factor},\n",
+    "    rebalance_freq='M',\n",
+    ")\n",
+    "\n",
+    "print(f'回测完成, 共 {len(equity_curve)} 个交易日')\n",
+    "print(f'累计收益率: {(equity_curve[\"nav\"].iloc[-1] - 1) * 100:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. 净值曲线与回撤"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, axes = plt.subplots(2, 1, figsize=(14, 8))\n",
+    "\n",
+    "# 净值曲线\n",
+    "axes[0].plot(equity_curve.index, equity_curve['nav'], label='策略净值', color='purple')\n",
+    "axes[0].axhline(y=1.0, color='gray', linestyle='--')\n",
+    "axes[0].set_title('价值投资策略净值曲线')\n",
+    "axes[0].legend()\n",
+    "axes[0].grid(True, alpha=0.3)\n",
+    "\n",
+    "# 回撤曲线\n",
+    "nav = equity_curve['nav']\n",
+    "running_max = nav.cummax()\n",
+    "drawdown = (nav - running_max) / running_max\n",
+    "axes[1].fill_between(equity_curve.index, 0, drawdown.values, color='red', alpha=0.3)\n",
+    "axes[1].set_title('回撤曲线')\n",
+    "axes[1].grid(True, alpha=0.3)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6. 绩效评估"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 基准：股票池等权组合\n",
+    "bench_ret = price_data.pct_change().mean(axis=1)\n",
+    "\n",
+    "evaluator = PerformanceEvaluator(equity_curve, benchmark_returns=bench_ret, risk_free_rate=0.03)\n",
+    "print(evaluator.summary())\n",
+    "\n",
+    "report_df = evaluator.full_report()\n",
+    "pd.DataFrame(list(report_df.items()), columns=['指标', '数值']).set_index('指标')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## 单独估值指标对比\n",
+    "\n",
+    "对比单独使用 EP、BP 与合成价值因子的效果："
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 单独因子的 IC 对比\n",
+    "for name, fac in [('EP', ep), ('BP', bp), ('EP+BP', value_factor)]:\n",
+    "    a = FactorAnalyzer(fac.stack(), fwd_stacked)\n",
+    "    s = a.ic_summary()\n",
+    "    print(f'{name}: IC均值={s[\"IC_Mean\"]:.4f}, ICIR={s[\"IR\"]:.4f}, '\n",
+    "          f'IC>0占比={s[\"IC>0_Ratio\"]:.2%}')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}